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pdftitle= J(O 1 D) at Cape Grim 2000 - 2005
pdfauthor=S.R. Wilson
title Manuscript prepared for J. Name
with version 4.1 of the LATEX class copernicus discussions.cls.
Date: 28 October 2014
Characterisation of J(O 1D) at Cape Grim
2000 - 2005
S.R. Wilson1
1
Centre for Atmospheric Chemistry, School of Chemistry, University of Wollongong, NSW,
Australia
Correspondence to: Stephen Wilson
([email protected])
1
Abstract
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abstr Estimates of the rate of production of excited oxygen atoms due to the photolysis of ozone
J(O 1D ) have been derived from radiation measurements carried out at Cape Grim, Tasmania
(40.6S, 144.7E). The measurements have a total uncertainty of ¡ 25 %. The photolysis estimates
agree well with model estimates of clear sky photolysis rates. Observations spanning 2000 2005 have been used to quantify the impact of season, clouds and ozone column amount. The
annual cycle of J(O 1D ) has been investigated via monthly means. These means show an interannual variation (monthly standard deviation) of 9%, but in midsummer and midwinter this
reduces to 3 - 4%. Variations in solar zenith angle and total oxone column explain 86% of the
observed signal variation of the individual measurements. The impact of total column ozone,
expressed as a Radiation Amplification Factor (RAF), is found to be ∼1.43, in agreement with
model estimates. This ozone dependence explains 20% of the variation observed at medium
solar zenith angles (30 - 50◦ ). The impact of clouds results in a median reduction of 22% in
J(O 1D ) for the same solar zenith angle range. At all solar zenith angles less than 50◦ approximately 1% of measurements show enhanced J(O 1D ) as determined by comparisons with a
clear sky model. Apparently due to cloud scattering and this fraction climbs to 25% at larger
solar zenith angles. Including estimates of cloudiness derived from Long Wave Radiation measurements resulted in a statistically significant fit to observations but the quality of the fit did
not increase significantly as measured by the adjusted R2 .
1
Introduction
introIt is widely recognised that the chemistry of the clean troposphere is driven by a few key
oxidizing species, with a major contributor being the hydroxyl radical (OH) (Crutzen, 1974).
The hydroxyl radical reacts rapidly with a wide range of compounds, including methane, CO,
and hydrocarbons. The concentration of OH present in the atmosphere is always small, but
because of its high reactivity it can still play a dominant role in determining the atmospheric fate
of organics. It has also long been realised that changes in the amount of OH in the atmosphere
2
5
could have a profound effect on global air quality and there has been a long-term effort to
develop techniques to measure the key chemical species (Heard and Pilling, 2003). The most
direct measure is the concentration of OH itself. There are several techniques now in use for
such measurements, including fluorescence, UV absorption and mass spectrometry (Heard and
Pilling, 2003) although only one long term measurement set has been reported to our knowledge
(Rohrer and Berresheim, 2006).
The primary source of OH is through the photolysis of ozone to produce O( 1D ) through the
reactions:
J(O 1D )
O3 + hν −−−−−→ O(1 D) + O2
1
∆g , 3 Σ−
g
(R1)
k
10
3
O(1 D) + H2 O −→
2OH
(R2)
k
2
O(1 D) + M −→
O(3 P )
(R3)
The fraction of O( 1D ) reacting with water (and hence producing OH) (Q) is given by:
X
Q = k3 [H2 O]/( k3 [H2 O] +
ki [Mi ])
(1)
i
15
20
Here the summation is over the collision partners Mi , primarily O2 and N2 . Q depends on the
amount of water vapour, but typically around 10% of O( 1D ) produced reacts to form OH (Q ≈
0.1). This can be calculated provided atmospheric pressure and the water vapour concentration
is known, since the rate constants have been measured (Sander et al., 2006).
The rate of ozone photolysis in reaction (1), J(O1 D), can be described by:
Z
1
J(O D ) = σ(λ, T )φ(λ, T )F (λ)dλ
(2)
which is the wavelength integration of σ(λ, T ), the (temperature dependent) absorption crosssection of ozone, φ(λ, T ), the quantum yield of O(1 D) production, and F (λ), the “spectral
3
actinic flux density”, which is the spherically integrated spectral radiance. There are a number
of measurements of J(O 1D ) via chemical actinometers (Hofzumahaus et al., 2004), although
due to their nature they are difficult to deploy for long periods of time, making either filter
radiometers or spectral radiometers an attractive alternative (Bohn et al., 2008).
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1.1
Techniques for the measurement of actinic flux density (F )
There are a range of radiometric techniques used for the determination of actinic flux, and the
strengths of various detectors has been assessed by a field comparison experiment (Bohn et al.,
2008). All these techniques relied on calibrations using reference light sources.
1.1.1
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Estimating actinic flux density from irradiance measurements
The ideal viewing geometry for the determination of F (λ) detects photons from all directions
equally (all 4π steradian). For locations not over reflective surfaces like snow the upwelling
radiation is relatively small, and so most measurements of F (λ) are made viewing down-welling
radiation only (e.g., Junkermann et al., 1989).
Most quantitative UV observations measure global irradiance (E) (the energy striking a horizontal plane), and so there have been a number of attempts to convert global irradiance into
actinic flux (Kazadzis et al., 2004; Kylling et al., 2003; McKenzie et al., 2002; Schallhart et al.,
2004; Webb et al., 2002).
If it is assumed that there is no upwelling radiation (surface albedo = 0), the actinic flux is
given by
F = F0 + F↓
(3)
where F0 is the direct actinic flux density and F↓ is the diffuse component. Similarly, the
global irradiance (E) is given by
E = µE0 + E↓
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(4)
where E0 is now the direct beam irradiance, µ = cos θ where θ is the solar zenith angle and
E↓ is the diffuse irradiance. As E0 = F0 , it is now possible to simply write
4
F = αE↓ + E0 = α(E − E0 ) + E0
(5)
where α is the ratio of the diffuse actinic flux density to diffuse irradiance. If the diffuse irradiance is not measured, equation (5) can be rearranged into the following relationship suggested
by Kazadzis et al. (2004).
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E0
F
= α + (1 − αµ)
E
E
The ratio α needs to be determined at the wavelengths relevant for the O( 1D ) photolysis
(McKenzie et al., 2002; Webb et al., 2002), and this will be discussed in Section 3.1.
Estimating the ratio of the direct beam to global irradiance (E0 /E) has been more difficult.
Schallhart et al. (2004) have therefore used a semi-empirical method which parameterized the
relationship (F/E) based on the ratio of observed irradiance to clear sky irradiance, where the
clear sky irradiance is calculated. Using data from four locations they found their results gave
better agreement between measured and calculated F (7%, 2σ) than that reported using equation
6 and no knowledge of the direct to global irradiance ratio (Kylling et al., 2003; Webb et al.,
2002). Using global irradiance measurements combined with direct irradiance every 10 nm,
Kazadzis et al. (2004) estimate an overall uncertainty of around 10% (1σ).
1.1.2
20
(6)
Wavelength Sensitivity of Measurements
Three types of measurement detector have typically been used; a filter radiometer, a scanning
spectrometer or a diode array/ CCD detector equipped spectrometer system (Bohn et al., 2008;
Hofzumahaus et al., 2004). Each approach has limitations. The filter radiometer measures at a
fixed wavelength range, which needs then to be calibrated using the actual atmospheric ozone
column and solar zenith angle factors (Bohn et al., 2004). The scanning spectrometer takes time
to scan through the spectrum, rather than measuring at a fixed time, leading to measures that are
“time-smeared” rather than “time-averaged”. For the production of a short-lived species like
5
O(1 D) this can lead to difficulties in comparing with other measurements. Finally, the diode
array/CCD system needs to have well characterized stray-light corrections applied (Bohn et al.,
2008; Hofzumahaus et al., 2004; Edwards and Monks, 2003).
1.2
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The Cape Grim Baseline Air Pollution Station (“Cape Grim”), (40◦ 40’ 56”S, 144◦ 41’ 18”E) is
a site near the northwest tip of Tasmania that experiences periods of clean maritime air from
the southern ocean. During two intensive measurement campaigns SOAPEX - 1 (1995) (Monks
et al. (1998)) and SOAPEX - 2 (1999) (Creasey et al., 2003) filter radiometers have been deployed to measure J(O 1D ). During SOAPEX -2 the OH concentration was also measured. The
measurements during the second campaign clearly demonstrated a simple link between O( 1D )
production and OH concentrations in clean atmosphere conditions (Creasey et al., 2003).
As part of the Cape Grim measurement program spectral UV-B irradiance (both global and
diffuse) has been measured routinely. The purpose of this work is to use the spectral UV-B
measurements to estimate for the period 2000 - 2005, to assess estimates of the photolysis rates
and to then develop a climatology. In particular, the impact of clouds and ozone will be assessed.
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Estimates of J (O 1D ) at Cape Grim
Experimental setup
All UV-B irradiance measurements reported here have been made in the radiation enclosure at
the Cape Grim Baseline Air Pollution Station. This is located some 300m north of the main
building (Cainey et al., 2007). The location avoids the shadow of the telecommunication tower
that is situated just to the north of the main building. The experimental details of the UVB measurements and in-situ calibration technique have been reported elsewhere (Wilson and
Forgan, 1995; Wilson, 2006, 2007). In brief, the system alternately measures global and diffuse
irradiance with a scanning double monochromator (Optronics Laboratories OL752) known as
SRAD. Diffuse irradiance is measured by shading the global diffuser with a small shading disk
mounted on the elevation arm of a tracker (Wilson, 2006). The spectral scans are spaced at 5
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- 10 minute intervals, depending upon the time of day. The instrument is calibrated at 342 nm
using well characterized sunphotometer measurements of direct beam irradiance, and the other
wavelengths calibrated using the Ratio-Langley technique (Wilson and Forgan, 1995). All this
is referenced to a top of the atmosphere spectrum (Chance and Kurucz, 2010) which serves as
the primary calibration of both wavelength and intensity. The optical input for the system was
modified in October 1999, resulting in higher optical throughput (and hence better signal/ noise
ratios) and a diffuser with a better cosine response. The focus of this work is therefore on the
period after the change in diffuser.
It is worth noting that the cosine error of the diffuser is determined from the solar zenith angle
dependence of the ratio of the SRAD direct beam irradiance to the sunphotometer. A correction
for this variation can then be applied during the calibration.
The resulting database of measurements includes alternating estimates of global and diffuse
irradiance at each wavelength and time. The determination of the components of the irradiance
at a single time is based on interpolation of the (e.g. diffuse) measurements before and after the
(e.g. global) measurement in question (Wilson and Forgan, 1995), and so the derived signals are
an approximation of the value for the 10 - 20 minute period around the nominal measurement
time.
The input diffusers was constructed from PTFE but was not temperature controlled. The
phase change reported for this material at around 292K (Ylianttila and Schreder, 2005) is therefore a source of uncertainty in these measurements. This will also impact upon the calibration,
so that this will be at least partially captured in the variability of the calibrations.
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3.1
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Methodology
Derivation of J (O 1D ) production from UV-B measurements
As the Cape Grim UV data set includes both the diffuse and global irradiance, equation 5 can
be used, as the direct beam irradiance can be derived from the difference between the global
and diffuse (see equation 4). This leaves the determination of the ratio (α). For the wavelength
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region of interest (300 - 330 nm), a value of 2.0 could be used, which is the value appropriate
for isotropic radiation (McKenzie et al., 2002) and clear skies. In cloudy conditions α decreases
to values typically around 1.7 - 1.8. The calculations have been carried out using both the clear
sky estimate of α and a value of 1.73, typical of cloudy conditions (Kylling et al., 2003) . For
the analysis here the values using the lower value for α have been used unless otherwise noted.
For the ozone absorption cross section (σ (λ, T ) , equation 2) the measurements of Malicet
et al. (1995) at 22 ◦ C have been used, in conjunction with the temperature dependent O 1D
quantum yield (Sander et al., 2006), derived using the hourly average air temperature measured
at Cape Grim (as part of the meteorology program) (Cainey et al., 2007).
The uncertainty in these derived J(O 1D ) values have been estimated, with details given
in the supplementary information. In brief, the irradiance measures are estimated to have an
uncertainty (1σ) of 9 - 12%. The combined uncertainty of all terms is found to be around 25%.
The UV-B measurements span the region 298 - 335 nm, and this can lead to an underestimate
of the photolysis rate. A study by Jaekel et al. (2006) found that cut-offs below 298 nm did
not perturb the estimate of J(O 1D ) by more than 5%, with the maximum error at times of low
column ozone and high sun. Test measurements using spectra measuring out to 340 nm found
that including the region between 335 – 340 nm altered J(O 1D ) by less than 1%. There is
no recommended quantum yield for O 1D above 340 nm (Sander et al., 2006). The estimates
presented here will therefore be biased low by the limited wavelength coverage by typically less
than 5%.
The calibration uncertainty of the measurements is estimated to be 5% based on the uncertainties in the top of the atmosphere spectrum used as the calibration (Chance and Kurucz,
2010), and the variability in the calibration observed from the multiple calibrations carried out
during the 6 years, implying a total uncertainty of around 8% in F(λ). This does not include the
impact of model assumptions including the assumption of isotropic diffuse irradiance.
3.2
Modeling J (O 1D )
In the analysis of data the model TUV version 5.0 has been used (Madronich and Flocke, 1997).
One of the changes in this version of the model is the use of the same solar spectrum (Chance
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and Kurucz, 2010) as that used for the calibration of SRAD. The calculations have been run
at a range of solar zenith angles using an aerosol optical depth of 0.05 at 550 nm, a value
typical of conditions at Cape Grim (Wilson and Forgan, 2002). Because the model uses the
same spectral data (top of the atmosphere spectrum, ozone cross section, quantum yield) the
agreement between measurements and model should be close to the uncertainty in F , which is
estimated to be around 12% (Supplementary material).
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Comparison of measured J (O 1D ) with model estimates
The measurements can be compared with the clear sky calculations performed using TUV 5.0.
The results of this are shown in Figure 1, where the experimental values have been estimated
using a clear sky estimate for α and the cloudy value. It can be seen that for the data from
both February 2000 (low column ozone) and October 2003 (high column ozone) there is good
agreement between model and measurement (average deviation 2%) if the clear sky estimate
of α is used. Differences at high sun are around 3%. Several days exist where the irradiance
appears to vary smoothly but with differences of up to 10% at solar noon. While this could be
due in part to the limited measurement range (Section 3.2), a calibration issue that occurs at
these solar zenith angles, or due to clouds. The smooth change in J(O 1D ) implies no clouds
near the sun, but there can be clouds well away from the sun that are altering the observed
photolysis rate. Without a measure of the cloud field it is hard to distinguish between these
possibilities.
4.2
25
Results and Discussion
Annual Cycle in J (O 1D )
The data collected for the period 2000 - 2005 are shown in Figure 2. The dataset comprises over
108,000 measurements. The gaps in the dataset represent times when the equipment failed.
The annual cycle is the dominant feature in this plot. To quantify this, monthly mean values
have been calculated by sorting all data from a month into 24 hourly bins, and from these bins
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producing an average daily cycle for each month. It is assumed that if no measurement is made
in one of the 24 hourly bins during the month that the average is zero. The average of the 24
hourly averages is then calculated for each month in the 6 years. This method has been used to
limit the impact of possible biases from collecting spectra at varying time intervals.
Despite the variability seen in the individual measurements (See Figure 2), the monthly averages are relatively stable (Figure 3, top panel). The lower panel of Figure 3 shows that for midsummer and mid-winter the interannual variability in the monthly averages is 3-4%, with the
increases in-between presumably driven by the rate of change of the solar zenith angle at midday, the factor also driving much of the observed annual cycle. The resultant average monthly
J(O 1D ) for Cape Grim is also presented in Table 1 along with the standard deviations.
Measurements of J(O 1D ) have been reported for 2002 - 2006 in the eastern Mediterranean
(Gerasopoulos et al., 2012). The interannual variability in the monthly mean maxiumum clear
sky irradiance is of the order of 7 - 8%. This is comparable to the monthly relative standard
deviation in all measurements at Cape Grim (9.2%). The two locations are very different, both
in terms of aerosol loadings and cloudiness, so the similarity is not expected.
Earlier measurements of interannual variability of UV-B have been reported for Ushuaia in
Argentina (Frederick et al., 2001). For global irradiance at 305 nm they found an interannual
variability (standard deviation/ average) of around 25%. The variability in global irradiance
could be expected to be bigger than that for J(O 1D ) with the different dependence on the
angle of incidence of radiation. The mean of the monthly relative standard deviation (9.2%) is
indeed slightly lower than that observed for global UV-B irradiance (10.8%) as determined from
the Cape Grim data. However, both are significantly less than reported from Argentina. This is
presumably a reflection of the difference in climate, with Cape Grim routinely experiencing
cloudy conditions.
To investigate any trend in the data both monthly trends for each month and trends as a
function of season have been calculated. The most significant linear trend is in summer (Dec
- Feb) (−1.7 ± 1.1 (std. dev.) %/year), but this is not significant at the 90% level. Satellite
estimates of changes in irradiance at 305 nm due to stratospheric ozone and clouds at this
latitude are 0.3-0.4%/ year (averaged over 1979 - 2008) (Herman, 2010). For the shorter period
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measured here it is not possible to detect changes of that magnitude, and local effects on clouds
could determine the magnitude (and sign) of the observed trend.
4.3
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Ozone Column Dependence
The dependence of J(O 1D ) on solar zenith angle has been determined by sorting all data into
5◦ bins, and the results are summarized in Figure 4. For this plot, zenith angles up to 82.5◦
have been included. All measurements have been adjusted to 1 a.u. (correction for the annual
variation in the earth-sun distance, (Iqbal, 1983)). A few measurements made at solar zenith
angles less than 17.5 degrees have been excluded as they represent a brief period in mid-summer.
Included in the plot are J(O 1D ) estimates calculated using the TUV model for cloud free
conditions and an aerosol optical depth of 0.05. Calculations for two ozone column amounts are
shown, 250 and 350 DU, which are typical seasonal maximum and minimum values observed in
this location as derived from satellite measurements (TOMS) (http://toms.gsfc.nasa.gov/index
v8.html).
A significant fraction of the variability can be due to the differences in the ozone column
during the year. To characterize the dependence, functions of the following form were fitted to
the the measured J(O 1D ) values.
!
1
J(O D) =
X
sat
−RAF
Ai exp(−Bi / cos θ) . O3 300
(7)
i
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where θ is the solar zenith angle, O3sat is the total ozone column retrieved from satellite for the
measurement day, and Ai , Bi and RAF are fitted. RAF is the Radiation Amplification Factor to
be determined (Micheletti et al., 2003). The results for the fit to the entire dataset using either
one or two exponential terms (i = 1 or 2) are shown in table 2 and for one exponential term in
Figure 4. Using two exponentials produces a slightly better fit, and both fits produce an RAF
estimate in excellent agreement with calculations of 1.4 - 1.5 (McKenzie et al., 2011).
Using this derived ozone RAF the dataset was normalized to both 300 DU and 1 a.u. as shown
in Figure 5. Given the large difference between the median and average values for the bins, a
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second fit was performed to the median of the binned values of Figure 5, and the fits are also
included in Table 2. For reference, the fits with two exponential terms, using all data and the
medians is included in Figure 5. It should be noted that the increase in R2 is due to the change
in the nature of the data being fitted.
The removal of the variation due to changes in stratospheric ozone, as described by the satellite ozone measurements, reduces the interquartile variability by up to 20% as shown in Figure
6. The effect on high sun (small solar zenith angle) measurements is smaller, as this is only
collected in mid-summer and so the ozone variability is small. At larger solar zenith angles
(>50◦ ) the percentage reduction diminishes also, presumably because, as the absolute intensity
decreases other effects, including the impact of measurement uncertainty, become larger.
4.4
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Cloud Impact
Clouds can both reduce and enhance solar radiation at the ground level. Figure 5 shows that the
99 th percentile value closely follows the clear-sky calculated value at solar zenith angles less
than 50◦ . This could be an aretfact due to biases in the clear sky calculations or measurement.
If real, this implies that approximately 1% of these measurements show an enhancement of
radiation due to clouds, a phenomenon often observed (Calbo et al., 2005). The likelihood
of this cloud enhancement appears to increase with increasing solar zenith angle with up to
25% of measurements showing an enhancement by 65◦ , presumably as a result of the changes
in scattering geometry in the atmosphere. This could also be due to errors in the model or
unidentified cosine errors. However, the question does arise as to whether such behaviour is
consistent with known cloud impacts on radiation.
To assess the overall impact of clouds, the ratio of the median value to the calculated clear
- sky value was determined (Figure 7). This shows that for solar zenith angles less than 70◦ the
median is approximately 75% of the calculated clear sky value. From 20 - 70◦ the calculated
impact of clouds on J(O 1D ) increases by 5 - 6%, a trend also predicted in models of the cloud
impact on UV irradiance (Lindfors and Arola, 2008).
The results for solar zenith angles greater than 70◦ suggest that clouds have a diminishing
impact as the sun approaches the horizon, as noted at other locations (Mateos et al., 2014). This
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can be a result of the increasing importance of scattered light under these conditions due to the
longer atmospheric path for the direct beam. As scattered radiation has become more significant,
it could be expected that clouds more readily enhance the observed radiation (Figure 5) to the
point that their overall impact is small (Figure 7). It can be concluded that the behaviour shown
in Figure 7 is consistent with other measures of cloud properties. However, both measurement
uncertainties (smaller signals and variations in detector angular response) and modelling limitations could be playing a significant role. The enhancement in inter-quartile range, also shown
in Figure 7, could also be due to a combination of cloud impact or measurement uncertainty.
Attempts to capture the cloud variability through independent observations have not been
very successful. Measures such as visual observations and automatic sky cameras have not
been implemented at Cape Grim. While sunphotometers make measurements during this period, they do not make measurements of cloud optical depth as has been used elsewhere (Anton
et al., 2012). Longwave downward radiation (LDR) measurements have been used to estimate
cloudiness (Marty and Philipona, 2000; D¨urr and Philipona, 2004). The attraction of this measure is that LDR is relatively insensitive to the solar position, and so should be independent
of the other factors influencing J(O 1D ). An attempt at using LDR has been made using halfhourly long-wave radiation averaged values measured at Cape Grim (Wilson and Shinkfield,
2007) to derive the Clear-Sky Index (Marty and Philipona, 2000). In this case it was possible to
produce a fit extending equation (8) with an additional term (Clear-Sky Index)α , where α is a
fitted parameter. Fitting the entire dataset where LDR values were available returned a significant value for the exponent (−0.19 ± 0.01). However, the fit did not improve significantly, as
measured by the adjusted R2 (increases of ˜0.0005). The lack of improvement in the fit suggests
that this is not a useful approach. This could be due to the insensitivity of long-wave radiation
measures to higher-level clouds (Schade et al., 2009; Boers et al., 2010). However, cloud bases
are often low at Cape Grim (800 - 1000m) as observed by LIDAR measurements, (Young, 2007)
and so LDR should be a reasonable measure. It is more likely that the features of clouds that
cause changes in the observed LDR are not simply related to those features which result in a
significant reduction (or enhancement) of J(O 1D ).
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5
A dependence on aerosols has been identified in measurements of J(O 1D ) in the eastern
Mediterranean (Gerasopoulos et al., 2012) which could be part of the variation identified here
as a cloud impact. However, the low aerosol optical depth (mean of 0.07 at 500 nm compared
with 0.23 in the Meditteranean (Gerasopoulos et al., 2011) makes this a small effect, especially
when compared to the very common cloud cover at Cape Grim.
4.5
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Wider Relevance of the Observations
The atmospheric composition at Cape Grim is dependent on wind direction and clean or “baseline” conditions are defined by standard measures (Downey, 2007). The impact of the photolysis
measured here on the chemical composition of the atmosphere will depend on whether the local
atmosphere is clean or polluted. However, an analysis of the data presented here filtered for
only those measurements collected under “baseline” conditions gives results not statistically
different from those observed for the entire dataset. As the baseline selection process eliminates
a significant fraction of the data, the variability does increase.
Another important question is how reliably the climatology measured here is representative
of a larger region. Cape Grim, sitting on the coast could have a cloud environment different
to locations out to sea and inland. A study of the global irradiance at a number of locations
concluded that Cape Grim experienced cloud conditions similar to the southern ocean in this
area (Bishop et al., 1997), Studies of the clouds above Cape Grim showed that the cloud base is
typically above 800m, well above the station (Young, 2007) and a study of rainfall has shown
that while rainfall varies when moving inland it is reasonably constant along the coast (Jasper
and Downey, 1991). The ISCCP dataset (http://isccp.giss.nasa.gov/index.html) shows that cloud
amount at this latitude band over the oceans is 80 - 90%, with little dependence on longitude and
without an obvious trend over the period 1984 - 2008. Therefore, the cloud impacts observed at
Cape Grim should be representative of the marine environment at these latitudes.
Modelling studies (Liu et al., 2006) calculated that the impact of clouds on J(O 1D ) is around
8% averaged throughout the troposphere, but that ground level impacts are larger, of the order
of -20%. The data presented here shows an impact of clouds on J(O 1D ) consistent with that
estimate. The reduction in J(O 1D ) is half that often observed for global UV irradiance at 80
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- 90 % cloud cover, underlining the relative insensitivity of actinic flux to clouds (Calbo et al.,
2005). This is a result of the relative importance of diffuse radiation to the photolysis rate, and
the limited impact of clouds on total diffuse irradiance (Blumthaler et al., 1994).
The results of this study permit the prediction of J(O 1D ) in the current climate. The impact of stratospheric ozone recovery should be well described by our current understanding.
However, it would be useful to estimate the likely impact of future changes in cloud properties
on J(O 1D ). With the reasonable agreement between models and observations seen at Cape
Grim there can be some confidence in their predictions. For the maritime environment investigated here the overall impact of clouds is relatively small (15 - 20%) given the 80-90 % cloud
cover. Any future climate changes would need to change the frequency of clouds significantly
to alter J(O 1D ) greatly. Other changes, such as a change in cloud optical depth may be more
significant. Verifying any such changes in J(O 1D ) will require ongoing observations.
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Conclusions
conclusionsSix years of estimates of J(O 1D ) are presented for a clean southern hemisphere
marine site. The impact of solar zenith angle and total column ozone can be clearly seen and
quantified and the stratospheric ozone dependence is in good agreement with radiation model
estimates. The impact of clouds can also be characterized, with bounds on the impact of clouds
determined as a function of solar zenith angle. However, attempts at modelling the impact of
clouds using independent radiation measurements (Longwave Downward Radiation) produced
fits that did not significantly improve the quality of the model. So while the impact of clouds
can be quantified, a good proxy for this has proven elusive.
Acknowledgements. This work would not be possible without the ongoing dedication and support of the
staff at the Cape Grim Baseline Air Pollution Station and the financial support provided for work at Cape
Grim by the Bureau of Meteorology. The inspiration of the other scientists involved in the Cape Grim
programme is also gratefully acknowledged. The detailed and constructive comments from the referees
have significantly improved this paper.
15
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References
references
20
-5
5 x 1 0
-5
4 x 1 0
-5
4 x 1 0
-5
3 x 1 0
-5
3 x 1 0
-5
2 x 1 0
-5
2 x 1 0
-5
1 x 1 0
-5
1 x 1 0
-5
1 F e b 2 0 0 0
4 O c to b e r 2 0 0 3
T U V m o d e l
1
J ( O D ) ( c le a r s k y )
1
J ( O D ) ( c lo u d )
J (O
1
D ) (s
-1
)
5 x 1 0
3 6 5 D U
2 6 2 D U
0
0
0 6 :0 0
1 2 :0 0
1 8 :0 0
0 6 :0 0
1 2 :0 0
1 8 :0 0
Fig. 1. Comparison of clear sky calculation values to measurements. Calculations have been performed
using TUV 5.0 with the column ozone amount reported by satellite. J(O 1D ) has been calculated using
an α of 1.96 relevant to clear skies (green line) and 1.73 (cloudy - black line).
figure
21
-5
4 .0 x 1 0
-5
2 .0 x 1 0
-5
J (O
1
D ) (s
-1
)
6 .0 x 1 0
0 .0
2 0 0 0
2 0 0 1
2 0 0 2
2 0 0 3
2 0 0 4
2 0 0 5
2 0 0 6
D a te
Fig. 2. Photolysis rate J(O 1D ) observed at Cape Grim 2000 - 2005. Gaps in the data are due to instrument failure.
22
-5
1 .5 x 1 0
M o n th ly a v e r a g e s
-5
5 .0 x 1 0
-6
J (O
1
D ) (s
-1
)
1 .0 x 1 0
0 .0
2 0
1 0
%
s td . d e v
1 5
5
0
0
2
4
6
8
1 0
1 2
M o n th o f y e a r
Fig. 3. Annual cycle of J(O 1D ). The bottom panel shows the scatter in the monthly values as a percentage of the monthly mean.
23
6 .0 x 1 0
-5
2 5 0 D U
3 5 0 D U
F it to a ll d a ta , s in g le e x p o n e n tia l
-5
2 .0 x 1 0
-5
C o rre c te d to 1 a .u .
J (O
1
D )(s
-1
)
4 .0 x 1 0
85
80
75
70
65
60
55
50
45
40
35
30
25
20
0 .0
S o la r Z e n ith A n g le
Fig. 4. Solar zenith angle dependence of J(O 1D ). Crosses mark the 1 and 99 percentile. The boxes span
25/75%, the whiskers mark 10 and 90%, the central line indicates the median and the square the average
value. The x-axis value is the central value of the 5◦ bin used. Included are the calculated J(O 1D )
(TUV (V 5.0)) for ozone column amounts of 250 DU (February) (black solid line) and 350 DU (Sept)
(red dashed line). The short-dashed green line is the fit to a single exponential term to all data.
24
6 .0 x 1 0
-5
3 0 0 D U O z o n e T U V
D o u b le E x p o n e n tia l fit
F it to m e d ia n s
-5
2 .0 x 1 0
-5
J (O
1
D )(s
-1
)
4 .0 x 1 0
0 .0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
S o la r Z e n ith A n g le
Fig. 5. Cape Grim measurements of J(O 1D ) adjusted to a nominal 300 DU and 1 AU. The solid line is
the calculated J(O 1D ) for clear skies and 300 DU ozone column and an aerosol optical depth of 0.05
using TUV 5.0 (Madronich and Flocke, 1997). The two broken lines are the results of the fits to two
exponential terms to either all data or the median value of each bin (See also table 2).
25
In te r q u a r tile a s a fr a c tio n o f th e m e d ia n ( % )
U V B
J (O
J (O
8 0
1
D )
1
D ) O
3
C o rr
6 0
4 0
2 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
S o la r Z e n ith A n g le ( d e g )
Fig. 6. The interquartile (75%-25%) difference as a percentage of the median value as a function of solar
zenith angle. UVB is the global irradiance, and the other two terms are the derived photolysis rates, with
the red curve measurements have been corrected to a constant column ozone amount.
26
R a tio ( M e d ia n /T U V )
1 .0 5
1 .0 0
0 .9 5
0 .9 0
0 .8 5
0 .8 0
0 .7 5
0 .7 0
R a tio to M e d ia n
1 .4
3
1 .2
rd
Q u a r tile
s t
Q u a r tile
1 .0
0 .8
1
0 .6
2 0
3 0
4 0
5 0
6 0
7 0
8 0
S o la r Z e n ith A n g le ( d e g )
Fig. 7. Top panel shows the ratio of the median measured J(O 1D ) to that from a calculation for clear
skies. The bottom panel shows the spread in quartile values as a ratio to the median.
27
Table 1. Monthly mean photolysis rate J(O1 D). This is calculated using hourly averages for each of the
24 hours in the day.
table
Month
Mean (std. deviation)
s−1 × 10−6
1
2
3
4
5
6
7
8
9
10
11
12
10.59 (0.42)
9.02 (0.74)
5.69 (0.67)
3.06 (0..23)
1.72 (0.24)
0.90 (0.08)
0.96 (0.03)
1.65 (0.14)
3.18 (0.64)
5.12 (0.70)
8.03 (0.35)
10.40 (0.78)
28
Table 2. Results for fitting J(O1 D) with the form shown in equation 8. Brackets [] surround values that
have been assumed in the fit. Uncertainties (in brackets) are standard errors in the last quoted figure of
the fitted parameters. Units for A1 and A2 are s−1 . The ”median fit” is a fit to the medians as shown in
figure 6.
Fit
A1 /10−4
B1
All data
1.608(7)
4.6(1)
2.2(2)
4.4(5)
1.592(3)
2.92(4)
1.77(8)
2.7(1)
Medians
A2 /10−5
B2
2.4(1)
0.74(2)
1.8(5)
0.64(9)
29
RAF
R2
1.403(9)
1.426(9)
[1.43]
[1.43]
0.849
0.857
0.993
1.000
Manuscript prepared for Atmos. Chem. Phys.
with version 2014/07/29 7.12 Copernicus papers of the LATEX class copernicus.cls.
Date: 27 October 2014
Error Estimates for J(O 1D)
S.R. Wilson
Contents
1
Introduction
1
2
Actinic Flux Density Estimate (F )
2
2.1
Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2
2.2
Derived uncertainty in F . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3
2.2.1
c(λr ) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4
2.2.2
crel (λ) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5
2.2.3
c(λ) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6
Uncertainty in F . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6
5
2.3
2.4
10
1
1
Determination of J( O D) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6
Introduction
This document summarizes the error esimates for the Cape Grim measurements of J(O 1 D). These
measurements have been made using a spectrometer (SRAD) that takes alternate measurements of
global and diffuse irradiance. The photolysis rate (J) being estimated is defined by:
15
J(O 1D ) =
Z
σ(λ, T )φ(λ, T )F (λ)dλ
(1)
An explanation of the symbols used is given in Table 1. In this expression, we need to come up
with an estimate of the uncertainty in all 3 terms. These will be addressed individually below, starting
with the actinic flux density (F). This is the term that is driven by the Cape Grim measurements.
1
Table 1. Symbols used in this work.
Symbol
Meaning
S0
Direct beam signal from SRAD
S↓
diffuse signal from SRAD
S
global signal from SRAD
θ
solar zenith angle
F (λ)
spectral actinic flux density
σ(λ, T )
ozone absorption cross section
φ(λ, T )
quantum yield for (O 1D ) production
λr
Wavelength at which the reference sunphotometer operates
c(λr , θ)
calibration for the direct beam irradiance atλr
f (θ)
variation in instrument sensitivity
Section 2.1 summarises the calibration method and how F is derived from measurements. More
20
details can be found on the calibration in Wilson and Forgan (1995). Section 2.2 then looks at the
uncertainties in the determination of the irradiance, which is then combined to determine F . The
final section then comments on the integration of this term with the other components needed to
determine J.
Actinic Flux Density Estimate (F )
2
25
2.1
Theory
In summary, (F ) is determined from the global signal S0 and diffuse signal S↓ , measured alternately
and results interpolated to provide time coincidence :
S0 (λ) = (S(λ) − S↓ (λ))/cos(θ)
(2)
At the wavelength of a sunphotometer (λr ) this value can then be calibrated by a factor c(λr , θ)
30
(V /(W m−2 nm−1 ) derived from a calibrated sunphotometer measurement of direct beam irradiance
at a wavelength λr (Edir (λ)) collected at the same time as SRAD (solar zenith angle θ) :
c(λr , θ) = (Sdir (λr , θ)/(Edir (λr , θ))
(3)
The calibration will be assumed to be separable into two factors, with f (θ) encompassing the
variation in calibration due to an imperfect cosine response:
35
c(λr , θ) = c(λr ) ∗ f (θ)
(4)
2
At other wavelengths the relative calibration cRL (λ) is determined using the Ratio-Langley method
Wilson and Forgan (1995). This determines the ratio of the top of the atmosphere instrument signals:
cRL (λ) = Sdir 0 (λ)/Sdir 0 (λr )
(5)
So
40
0
0
c(λ) = cRL (λ) ∗ c(λr , θ) ∗ (Edir
(λr )/Edir
(λr ))
(6)
or
0
0
c(λ) = cRL (λ) ∗ c(λr ) ∗ f (θ) ∗ (Edir
(λr )/Edir
(λr ))
(7)
Presuming no azimuthal signal dependence, the diffuse irradiance at the sunphotometer wave-
45
length can be derived assuming isotropic diffuse sky irradiance from:
Z
S↓ /E↓ (λr ) = sin(2θ) ∗ Sdir /Edir (λr )dθ
(8)
or
Z
S↓ /E↓ (λr ) = c(λ)
sin(2θ) ∗ f (θ)(λr )dθ
(9)
Having calculated the direct and diffuse component, it is possible to determine the global irradiance from the direct and diffuse components:
50
E0 (λ) = Edir (λ) ∗ cos(θ) + E↓ (λ)
(10)
The Actinic flux density (F ) can be calculated from these measured parameters using the relationship:
F (λ) = E0 (λ) + αE↓ (λ)
2.2
55
(11)
Derived uncertainty in F
The primary calibration reference for these measurements is the sunphotometer, which for these data
is measuring at 342nm. This comparison provides both an estimate of the absolute calibration c(λr )
and an estimate of the measurement uncertainty associated with the global and diffuse irradiance.
For Cape Grim, the uncertainty in the sunphotometer measurements needs to be less than 1%, as
this corresponds to an uncertainty of 0.01 in aerosol optical depth. Aerosol optical depths at 342 nm
60
have a median magnitude of around 0.06.
3
2.2.1
c(λr )
The uncertainty in the measurements (S) can be most readily estimated from the variation in c(λr ).
This will contain the variability in both SRAD and the sunphotometer. The variation in this ratio
will be assessed from the data 25/2/2003 - 31/12/2005. Prior to this time there were some changes
65
in spectrometer configuration, altering c(λr ).
First, the solar zenith angle dependence needs to be considered. The ratio of spectrometer to
sunphotometer can be plotted as a function of solar zenith angle (Figure 1) The medians show a
maxiumum of 4 % deviation from the median at high and low solar zenith angles. The resultant
binned medians can be fitted to a polynomial dependendent upon θ. Note that including this in the
70
correction for the diffuse irradiance results in a change of 0.2 % .
1 2 0
f ( e le v a tio n d e p e n d e n c e )
1 1 5
1 1 0
1 0 5
1 0 0
9 5
9 0
18
23
28
33
38
43
48
53
58
63
68
8 5
S o la r Z e n ith A n g le
Figure 1. Ratio of derived direct beam irradiance to the sunphotometer signal, scaled by the median value for
this period. Data from 25/2/2003 onwards, sorted by solar zenith angle.
Next, we need to look at c(λr , θ) which is directly derived from the two measurements. Assuming
that the calibration factor for the period 2003 - 2005 is constant returns a median value for the
calibration with a 95% confidence interval of less than 1% for the median value. The ratio of the
signal from the two instruments displays a distribution is distinctly non-normal (using tests like
75
the Shapiro-Wilk), so that care needs to be taken in estimating parameters. The median absolute
deviation (MAD) of these ratio scaled to standard deviation equivalence, is 11.8 % of the median.
In order to assess the uncertainty in S and S↓ it has been assumed that they have a constant
relative uncertainty. This is based on the ranging behaviour of the hardware and software. The relationship between the uncertainties in S and S↓ to the uncertainty in S0 is non-linear. A trial and error
80
method was therefore used, varying the assumed relative error in the diffuse and global irradiance
for each measurement to derive a relative error in the direct beam signal. This was done for every
4
measurement where a comparison with the sun photometer was possible. The median value of the
over 11,000 estimates of the uncertainty in S0 has been compared to the scaled MAD derived above.
On this basis it is estimated that the relative uncertainty in the original measurements is 5%.
85
2.2.2
crel (λ)
Typically during a year there are around 70 calibration periods (morning/ afternoon) that can be
used for a ratio- Langley calibration. A similar number of clear mid-day periods are used for calibration below 305nm (Wilson, 2006). The mid-day calibrations are relative to 312.1 nm, using an
ozone column amount derived from the B (305.5 nm/ 325.3 nm) and C (311.5 nm / 332.5 nm) wave90
length pairs, where the calibrations for all wavelengths have been derived from the Ratio-Langly
calibration. The calibration from the two types of Ratio-Langley method are joined at 312.1 nm.
Shown in Figure 2 are the average cRL (λ) and crel (λ) for 2003. The smoothing applied to crel (λ)
is necessary as the lineshape of the instrument is presumably not well captured by the Gaussian
assumed in deriving the top of the atmosphere irradiance. The derived crel (λ) does change with
95
time, and an annual average value has been used for each year. The ratio decreases throughout
the measurement period, but the change in calibration between the years is less than 3 %. The %
uncertainty in crel (λ) is shown in Figure 3 for 2003.
1 .0
R e s p o n s e r e la tiv e to 3 4 2 n m
2 0 0 3
0 .8
0 .6
0 .4
0 .2
M e a n c
R L
( λ) w i t h s t d e r r o r m e a n
( λ)
S a v itz k y - G o la y s m o o th ( 3 .5 n m
re l
c
w in d o w )
0 .0
3 0 0
3 1 0
W
3 2 0
3 3 0
a v e le n g th ( n m )
Figure 2. The Ratio-Langley calibration and the resultant relative sensitivity as a function of wavelength for
2003
The irradiance at the top of the atmosphere taken from Chance and Kurucz (2010). They ascribe
an accuracy to their irradiances of better than 5%. This figure will be used for both irradiances. It
100
is probable that the uncertainty in the irradiances are correlated, so that the true uncertainty in the
irradiance ratio is less than this.
5
2 0 0 3
1 4
1 2
%
1 0
%
S ta n d a r d d e v ia tio n
S td D e v . in m e a n
%
8
6
4
2
0
3 0 0
3 1 0
W
3 2 0
3 3 0
a v e le n g th ( n m )
Figure 3. The standard deviation in the retrieved Ratio-Langley calibration for 2003.
2.2.3
c(λ)
The uncertainty in the calibration at any wavelength (λ) (see equation 7) can now be derived as the
percentage uncertainties added in quadrature, giving an overall uncertainty of 14 % in the calibrated
105
value, with the majority of this being driven by the estimation of measurement noise determined from
the comparison of the direct beam measurements by SRAD and the sunphotometer. This estimate is
only weakly dependent on wavelength as the dominant term is the uncertainty at λr .
2.3
Uncertainty in F
The uncertainty in F as derived from Equation 11 will contain the calibration uncertainty discussed
110
above, and the uncertainty in α. Based on the work of Kylling et al. (2003) α should lie in a range
1.73 (cloud) - 1.96, where the values used in the calculation are for clear skies. This represents an
uncertainty of the order of 10 %, implying an overall uncertainty of the order of 18 %.
From above, the uncertainty in E0 is estimated to be approximately 12 %. The uncertainty in αE↓
is very similar. Given that the two terms in equation (11) are not uncorrelated, it has been assumed
115
that the uncertainty in F is comparable to the components (12 %).
2.4
Determination of J ( O 1 D)
The combined uncertainty for the cross section and quantum yield for the production of O 1 D) from
ozone is estimated to be 20 % by Sander et al. (2006).
The UV-B measurements span the region 298 - 335 nm, and this can lead to an underestimate of
120
the photolysis rate. A study by Jaekel et al. (2006) found that cut-offs below 298 nm did not perturb
6
the estimate of J(O 1D ) by more than 5%, with the maximum error at times of low column ozone
and high sun. Test measurements using spectra measuring out to 340 nm found that including the
region between 335 – 340 nm altered J(O 1D ) by less than 1%. There is no recommended quantum
yield for (O 1D ) above 340 nm Sander et al. (2006). The estimates presented here will therefore be
125
biased low by the limited wavelength coverage by typically less than 5%.
The resultant errors are summaries in Table 2. It should be noted that this does not include any
estimate of the impact of model assumptions including the assumption of isotropic diffuse irradiance.
For comparison with the model TUV 5.0 (Madronich and Flocke, 1997) some of the uncertainties
are common, as the same spectral data are used. Therefore the relevant uncertainty in comparing
130
with the calculation is close to the uncertainty listed for F .
7
Table 2. Error estimates for the determination of J(O1 D) from global and diffuse irradiance measurements
using SRAD.
Error source
% uncertainty
Ref wavelength calibration at 342 nm
1
Uncertainty in global and diffuse
5
Ratio Langley relative calibration at 300 nm
1
Top of the atmosphere spectrum
7
α estimate
10
F determination
12
1
O3 cross section/ (O D) quantum yield
20
Combined error
23
References
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measurements in the ultraviolet, visible, and near infrared, Journal of Quantitative Spectroscopy and Radiative Transfer, 111, 1289–1295, doi:10.1016/j.jqsrt.2010.01.036, 2010.
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Jaekel, E., Wendisch, M., and Lefer, B.: Parameterization of Ozone Photolysis Frequency in the Lower Troposphere Using Data from Photodiode Array Detector Spectrometers, Journal of Atmospheric Chemistry, 54,
67–87, doi:10.1007/s10874-006-9014-1, 2006.
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